The (non)malignancy of cancerous amino acidic substitutions

Docking-based approach for identification of mutations that disrupt binding between Bcl-2 and Bax proteins: Inducing apoptosis in cancer cells

BACKGROUND

Inducing apoptosis in cancer cells is an important step for the successful treatment of cancer patients. Bcl-2 is an antiapoptotic protein which determines apoptosis by interacting with proapoptotic members of the Bcl-2 family. Exome sequencing has identified Bcl-2 and Bax missense mutations in more than 40 cancer types. However, a little information is available about the functional impact of each Bcl-2 and Bax mutation on the pathogenesis of cancer.

METHODS

The mutational data from cancer tissues and cell lines were retrieved from the cBioPortal web resource. The 13 mutated Bcl-2 and wild-type Bax complexes with experimentally verified binding were identified from previous studies wherein, binding for all complexes was reportedly disrupted except one. Several protein-protein docking methods such as ClusPro, HDOCK, PatchDock, FireDock, InterEVDock2 and several mutation prediction methods such as PolyPhen-2, SIFT, and OncoKB have been used to predict the effect of mutation to disrupt the binding between Bcl-2 and Bax. The result obtained was compared with the known experimental data.

RESULTS

The protein-protein docking method, ClusPro, employed in the present study confirmed that the binding affinity of 11 out of 13 complexes decreases. Similarly, binding affinity computed for all the 10 wild-type Bcl-2 and mutated Bax complexes agreed with experimentally verified results.

CONCLUSION

Several methods like PolyPhen-2, SIFT, and OncoKB have been developed to predict cancer-associated or deleterious mutations, but no method is available to predict apoptosis-inducing mutations. Thus, in this study, we have examined the mutations in Bcl-2 and Bax proteins that disrupt their binding, which is crucial for inducing apoptosis to eradicate cancer. This study suggests that protein-protein docking methods can play a significant role in the identification of hotspot mutations in Bcl-2 or Bax that can disrupt their binding with wild-type partner to induce apoptosis in cancer cells.

Is cell migration a selectable trait in the natural evolution of cancer development?

Selective evolutionary pressure shapes the processes and genes that enable cancer survival and expansion in a tumour-suppressive environment. A distinguishing lethal feature of malignant cancer is its dissemination and seeding of metastatic foci. A key requirement for this process is the acquisition of a migratory/invasive ability. However, how the migratory phenotype is selected for during the natural evolution of cancer and what advantage, if any, it might provide to the growing malignant cells remain open issues. In this opinion piece, we discuss three possible answers to these issues. We will examine lines of evidence from mathematical modelling of cancer evolution that indicate that migration is an intrinsic selectable property of malignant cells that directly impacts on growth dynamics and cancer geometry. Second, we will argue that migratory phenotypes can emerge as an adaptive response to unfavourable growth conditions and endow cells not only with the ability to move/invade, but also with specific metastatic traits, including drug resistance, self-renewal and survival. Finally, we will discuss the possibility that migratory phenotypes are coincidental events that emerge by happenstance in the natural evolution of cancer. This article is part of a discussion meeting issue 'Forces in cancer: interdisciplinary approaches in tumour mechanobiology'.

A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) – structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated. Our approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer associated genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/.

Disease-related mutations predicted to impact protein function

Background

Non-synonymous single nucleotide polymorphisms (nsSNPs) alter the protein sequence and can cause disease. The impact has been described by reliable experiments for relatively few mutations. Here, we study predictions for functional impact of disease-annotated mutations from OMIM, PMD and Swiss-Prot and of variants not linked to disease.

Results

Most disease-causing mutations were predicted to impact protein function. More surprisingly, the raw predictions scores for disease-causing mutations were higher than the scores for the function-altering data set originally used for developing the prediction method (here SNAP). We might expect that diseases are caused by change-of-function mutations. However, it is surprising how well prediction methods developed for different purposes identify this link. Conversely, our predictions suggest that the set of nsSNPs not currently linked to diseases contains very few strong disease associations to be discovered.

Conclusions

Firstly, annotations of disease-causing nsSNPs are on average so reliable that they can be used as proxies for functional impact. Secondly, disease-causing nsSNPs can be identified very well by methods that predict the impact of mutations on protein function. This implies that the existing prediction methods provide a very good means of choosing a set of suspect SNPs relevant for disease.

Human genomic disease variants: a neutral evolutionary explanation

Many perspectives on the role of evolution in human health include nonempirical assumptions concerning the adaptive evolutionary origins of human diseases. Evolutionary analyses of the increasing wealth of clinical and population genomic data have begun to challenge these presumptions. In order to systematically evaluate such claims, the time has come to build a common framework for an empirical and intellectual unification of evolution and modern medicine. We review the emerging evidence and provide a supporting conceptual framework that establishes the classical neutral theory of molecular evolution (NTME) as the basis for evaluating disease- associated genomic variations in health and medicine. For over a decade, the NTME has already explained the origins and distribution of variants implicated in diseases and has illuminated the power of evolutionary thinking in genomic medicine. We suggest that a majority of disease variants in modern populations will have neutral evolutionary origins (previously neutral), with a relatively smaller fraction exhibiting adaptive evolutionary origins (previously adaptive). This pattern is expected to hold true for common as well as rare disease variants. Ultimately, a neutral evolutionary perspective will provide medicine with an informative and actionable framework that enables objective clinical assessment beyond convenient tendencies to invoke past adaptive events in human history as a root cause of human disease.

Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer

BACKGROUND AND OBJECTIVE

With recent breakthroughs in high-throughput sequencing, identifying deleterious mutations is one of the key challenges for personalized medicine. At the gene and protein level, it has proven difficult to determine the impact of previously unknown variants. A statistical method has been developed to assess the significance of disease mutation clusters on protein domains by incorporating domain functional annotations to assist in the functional characterization of novel variants.

METHODS

Disease mutations aggregated from multiple databases were mapped to domains, and were classified as either cancer- or non-cancer-related. The statistical method for identifying significantly disease-associated domain positions was applied to both sets of mutations and to randomly generated mutation sets for comparison. To leverage the known function of protein domain regions, the method optionally distributes significant scores to associated functional feature positions.

RESULTS

Most disease mutations are localized within protein domains and display a tendency to cluster at individual domain positions. The method identified significant disease mutation hotspots in both the cancer and non-cancer datasets. The domain significance scores (DS-scores) for cancer form a bimodal distribution with hotspots in oncogenes forming a second peak at higher DS-scores than non-cancer, and hotspots in tumor suppressors have scores more similar to non-cancers. In addition, on an independent mutation benchmarking set, the DS-score method identified mutations known to alter protein function with very high precision.

CONCLUSION

By aggregating mutations with known disease association at the domain level, the method was able to discover domain positions enriched with multiple occurrences of deleterious mutations while incorporating relevant functional annotations. The method can be incorporated into translational bioinformatics tools to characterize rare and novel variants within large-scale sequencing studies.

The structural impact of cancer-associated missense mutations in oncogenes and tumor suppressors

BACKGROUND

Current large-scale cancer sequencing projects have identified large numbers of somatic mutations covering an increasing number of different cancer tissues and patients. However, the characterization of these mutations at the structural and functional level remains a challenge.

RESULTS

We present results from an analysis of the structural impact of frequent missense cancer mutations using an automated method. We find that inactivation of tumor suppressors in cancer correlates frequently with destabilizing mutations preferably in the core of the protein, while enhanced activity of oncogenes is often linked to specific mutations at functional sites. Furthermore, our results show that this alteration of oncogenic activity is often associated with mutations at ATP or GTP binding sites.

CONCLUSIONS

With our findings we can confirm and statistically validate the hypotheses for the gain-of-function and loss-of-function mechanisms of oncogenes and tumor suppressors, respectively. We show that the distinct mutational patterns can potentially be used to pre-classify newly identified cancer-associated genes with yet unknown function.

Germline fitness-based scoring of cancer mutations

A key goal in cancer research is to find the genomic alterations that underlie malignant cells. Genomics has proved successful in identifying somatic variants at a large scale. However, it has become evident that a typical cancer exhibits a heterogenous mutation pattern across samples. Cases where the same alteration is observed repeatedly seem to be the exception rather than the norm. Thus, pinpointing the key alterations (driver mutations) from a background of variations with no direct causal link to cancer (passenger mutations) is difficult. Here we analyze somatic missense mutations from cancer samples and their healthy tissue counterparts (germline mutations) from the viewpoint of germline fitness. We calibrate a scoring system from protein domain alignments to score mutations and their target loci. We show first that this score predicts to a good degree the rate of polymorphism of the observed germline variation. The scoring is then applied to somatic mutations. We show that candidate cancer genes prone to copy number loss harbor mutations with germline fitness effects that are significantly more deleterious than expected by chance. This suggests that missense mutations play a driving role in tumor suppressor genes. Furthermore, these mutations fall preferably onto loci in sequence neighborhoods that are high scoring in terms of germline fitness. In contrast, for somatic mutations in candidate onco genes we do not observe a statistically significant effect. These results help to inform how to exploit germline fitness predictions in discovering new genes and mutations responsible for cancer.

Comparative analysis of germline and somatic microlesion mutational spectra in 17 human tumor suppressor genes

Mutations associated with tumorigenesis may either arise somatically or can be inherited through the germline. We performed a comparison of somatic, germline, shared (found in both soma and germline) and somatic recurrent mutational spectra for 17 human tumor suppressor genes, which focused upon missense single base-pair substitutions and microdeletions/microinsertions. Somatic and germline mutational spectra were similar in relation to C.G>T.A transitions but differed with respect to the frequency of A.T>G.C, A.T>T.A, and C.G>A.T substitutions. Shared missense mutations were characterized by higher mutability rates, greater physicochemical differences between wild-type and mutant residues, and a tendency to occur in evolutionarily conserved residues and within CpG/CpHpG oligonucleotides. Mononucleotide runs (≥4 bp) were identified as hotspots for shared microdeletions/microinsertions. Both germline and somatic microdeletions/microinsertions were found to be significantly overrepresented within the "indel-hotspot" motif, GTAAGT. Using a naïve Bayes' classifier trained to discriminate between five missense mutation groups, 63% of mutations in our dataset were on average correctly recognized. Applying this classifier to an independent dataset of probable driver mutations, we concluded that ∼50% of these somatic missense mutations possess features consistent with their being either shared or recurrent, suggesting that a disproportionate number of such lesions are likely to be drivers of tumorigenesis.

Structural and functional restraints on the occurrence of single amino acid variations in human proteins

Human genetic variation is the incarnation of diverse evolutionary history, which reflects both selectively advantageous and selectively neutral change. In this study, we catalogue structural and functional features of proteins that restrain genetic variation leading to single amino acid substitutions. Our variation dataset is divided into three categories: i) Mendelian disease-related variants, ii) neutral polymorphisms and iii) cancer somatic mutations. We characterize structural environments of the amino acid variants by the following properties: i) side-chain solvent accessibility, ii) main-chain secondary structure, and iii) hydrogen bonds from a side chain to a main chain or other side chains. To address functional restraints, amino acid substitutions in proteins are examined to see whether they are located at functionally important sites involved in protein-protein interactions, protein-ligand interactions or catalytic activity of enzymes. We also measure the likelihood of amino acid substitutions and the degree of residue conservation where variants occur. We show that various types of variants are under different degrees of structural and functional restraints, which affect their occurrence in human proteome.